Powernet A tech platform to optimize agriculture management and operations
Food production needs to grow rapidly! Advances in food production have been focused in specific silos...electricity, environment, and water.
60 APPLIED LIFE SCIENCES↗
SEARCH · Engineering Papers
Results for “Advanced Optimization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs
The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.
Artificial Intelligence (AI)↗
Advanced Materials and Additive Manufacturing for Phase Change Thermal Energy Storage and Management: A Review
Abstract Phase change materials (PCMs) can enhance the performance of energy systems by time shifting or reducing peak thermal loads. The effectiveness of a PCM is defined by its energy and power density—the total available storage capacity (kWh m −3 ) and how fast it can be accessed (kW m −3 ). These are influenced by both material properties as well as geometry of the energy systems; however, prior efforts have primarily focused on improving material properties, namely, maximizing latent heat of fusion and increasing thermal conductivity. The latter is often at the expense of the former. Advanced manufacturing techniques hold tremendous potential to enable co‐optimization of material properties and device geometry, while potentially reducing material waste and manufacturing time. There is an emerging body of research focused on additive manufacturing of PCM composites and devices for thermal energy storage (TES) and thermal management. In this article, the fundamentals and applications of PCMs are reviewed and recent additive manufacturing advances in latent heat TES for both the PCM composite and associated heat exchanger are discussed. A forward‐looking perspective on the future and potential of PCM additive manufacturing for TES and thermal management is provided.
25 ENERGY STORAGE↗
Differences In High Burnup Fuel Management Strategies to Minimize FFRD and Increase Economic Viability
The nuclear industry is pursuing approval of an increase in the length of the pressurized water reactor (PWR) cycle from 18 months to 24 months to reduce reactor downtime and enhance the economic competitiveness of nuclear energy. Such an increase in reactor cycle length will require that the maximum rod average burnup exceeds the current regulatory limit of 62 GWd/MTU, and it could peak at approximately 75 GWd/MTU, posing potential reactor safety and performance concerns. One such concern is that fuel fragmentation, relocation, and dispersal (FFRD) could occur during a severe loss-of coolant accident (LOCA) in which a fuel rod balloons and bursts, and pulverized fuel fragments are dispersed throughout the reactor’s primary coolant system. Previous analyses have identified which reactor operating conditions leave the core more susceptible to FFRD and have shown that FFRD susceptibility is strongly linked to fuel rod burnup and linear heat rate (LHR) history. The work described in this report uses an optimization strategy known as parallel simulated annealing (PSA) and a coarse mesh Purdue Advanced Reactor Core Simulator (PARCS) reactor physics model to develop two core fuel loading patterns, each with a different optimization objective. One core optimization maximized the core’s cycle length while still respecting regulatory limits on the radial peaking factor and soluble boron concentration with a peak rod average burnup of 75 GWd/MTU. The second optimization was aimed at minimizing FFRD susceptibility while still targeting a 24-month cycle length and respecting regulatory limits. PARCS model predictions were verified using the high-fidelity Virtual Environment for Reactor Applications (VERA). The two core designs were compared to highlight core design strategies to minimize FFRD susceptibility and to maximize economic viability.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
IEA PVPS Task 13 Techno-Economic Study of Bifacial Photovoltaic Systems on Single Axis Trackers
The International Energy Agency PVPS Task 13 has assembled an international team of photovoltaic (PV) researchers from government labs, academia, and industry to study the specific application of bifacial PV modules deployed on tracking systems. This configuration is of particular interest as it has been identified as the PV system design with the lowest Levelized Cost of Electricity (LCOE) for over 90% of the world [1]. As such, much research, development, and commercial activity are focused on further optimizing this system design for different regions and markets, including field deployment and advanced modeling activities. Despite these efforts, many challenges remain to overcome with this system design. This conference paper will report on several areas the team is working on as part of this project. The first part of this study focuses on extensive interviews with PV tracker companies (we have identified 35) and customers worldwide to identify emerging technology trends, design diversity, market environments, and the reliability and performance of these systems. We will provide a market summary of technologies such as advanced tracking algorithms, methods for dealing with topography within the PV plant, different system configurations and layouts, backside irradiance optimization from albedo enhancement, and ground sculpting. We will also review dual-use applications of tracked bifacial systems, such as Agrivoltaics and Building-Integrated PV. These interviews are underway now, and we will be able to report our initial findings at the conference. The second part of the study focuses on summarizing performance monitoring and evaluation methods, with particular attention to capacity and acceptance testing for such systems. The third part of the study reviews performance modeling and yield assessment methods for these tracked bifacial PV systems. The final part of the study covers reliability and operations, and maintenance data from these systems.
albedo↗
Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services
The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.
14 SOLAR ENERGY↗
Impacts and emerging research opportunities in Vehicle-Grid Integration for transportation: A review
This review provides a comprehensive examination of Vehicle-Grid Integration (VGI) technologies and their impacts on transportation systems, with a particular emphasis on the transportation-energy nexus. It systematically explores how VGI affects key transportation applications such as charging infrastructure planning, electric vehicle (EV) routing, smart charging coordination, shared mobility, and dynamic pricing. By synthesizing recent literature from both transportation and energy systems perspectives, this study highlights how advanced methodologies, such as reinforcement learning, game theory, and optimization techniques, are used to model the complex interactions between EVs, mobility patterns, and distributed energy systems. Furthermore, the review also identifies critical challenges, including behavioral factors, data limitations, and system scalability. Drawing on these insights, the paper outlines emerging research opportunities to support the design of integrated, resilient, and user-centric VGI solutions that advance sustainable mobility and energy system efficiency.
Charging coordination↗
Control co-design of commercial building chiller plant using Bayesian optimization
Not Available
Bhattacharya, Arnab↗
Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence
Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.
59 BASIC BIOLOGICAL SCIENCES↗
Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy
Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.
36 MATERIALS SCIENCE↗
The NREL Sensor Laboratory: Status and Future Directions for Hydrogen Detection
The NREL Hydrogen Sensor Laboratory was commissioned in 2010 as a resource for the national and international hydrogen community to ensure the availability and proper use of hydrogen sensors. Since then, the Sensor Laboratory has provided unbiased verification of hydrogen sensor performance for sensor developers, end-users, and regulatory agencies and has also provided active support for numerous code and standards development organizations. Although sensor performance assessment remains a core capability, the mission of the NREL Sensor Laboratory has expanded toward a more holistic approach regarding the role of hydrogen detection and its implementation strategy for both assurance of facility safety as well as for process control applications. Active monitoring for detection of unintended releases has been identified by the NFPA 2 Storage Task Group as a viable approach for improving facility safety and lowering setbacks. The current research program for the Sensor Laboratory addresses both conventional and advanced developing detection strategies in response to the emerging large-scale markets, such as those envisioned by H2@Scale. These emerging hydrogen applications may require alternative detection strategies that supplement and may ultimately supplant the use of traditional sensors for monitoring hydrogen releases. Research focus areas for the NREL Sensor Laboratory now encompasses the characterization of released hydrogen behavior to optimize detection strategies for both indoor and outdoor applications, advanced methods of hydrogen leak detection such as hydrogen wide area monitoring approaches for large scale and distributed applications, the incorporation of active monitoring as a risk reduction strategy to improve safety at hydrogen facilities, and continuing support of hydrogen safety codes and standards. In addition to assurance of safety, detection will be critical for process control applications, such as hydrogen fuel quality verification for fuel cell vehicle applications and for monitoring and controlling of hydrogen-natural gas blend composition.
codes and standards↗
Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches
The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.
09 BIOMASS FUELS↗
Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform
The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Economic Evaluation of a Coupled Nuclear Power Plant and Hydrogen Production Facility: A Case Study
This study optimized the design sizes and operation of a power-to-hydrogen-to-power integrated energy system to allow a baseload power plant to operate flexibly in the energy market. In collaboration with a utility industry partner, the system, consisting of an electrolyzer, compressors, storage tank, and fuel cell, was optimized under conditions specific to the proposed project at the site of a nuclear power plant. The Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) maximized net present value by optimizing sizing of components and dispatch decisions. Revenues included sale of electricity, capacity payments typical of the New York Independent System Operator, and the section 45V hydrogen production tax credit of the Inflation Reduction Act of 2022 (the tax credit was assumed to be available to legacy plants in the absence of clear guidance at present). Under default assumptions which excluded many capital expenditures, the base case optimized solution had a net present value of $\$$1.4 million over a 30 year lifetime, with a 0.365 MW fuel cell operating nearly continuously and 85% of revenues supplied by the hydrogen production tax credit (which was counted as a revenue regardless of profit, thus assuming credit monetization or offset of taxes within the larger firm was possible in all years). Beyond the base case, a sensitivity study elucidated drivers of the economics as capacity payment rate and hydrogen production tax credit rate vary. Additional sensitivity studies also extended results to variation of other, previously fixed parameters, including the fuel cell capital cost, and to imposition of further constraints. Optimization was also repeated for the default assumptions but recognizing tax credits upon use of hydrogen rather than upon its production, producing no change in the optimal solution. Most notably, capacity payments above $\$$15/kW-month drove optimal fuel cells multiple times larger than those with the default estimated capacity payment of $\$$2.5/kW-month (approaching 11 vs. 0.365 MW), and these larger fuel cells operated rarely (capacity factors of ~0.03). Furthermore, when the hydrogen production tax credit was provided for only 10 years, under the specific assumptions of this study (e.g., neither site preparation costs nor electrolyzer capital cost counted), the optimal solution avoided economic loss by ceasing system operation after the 10th year. Viewed broadly, this study demonstrated the capabilities of DISPATCHES, which can be user-adapted to serve other industrial case studies.
08 HYDROGEN↗
Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency
Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing
The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Tightest Mixed-Integer Programming Formulations for Quadratic SCUC Optimization
In this project, we developed new, tighter Mixed-Integer Programming (MIP) formulations for the combined Alternating Current (AC) Security-Constrained Unit Commitment (SCUC) and Security-Constrained Optimal Power Flow (SCOPF). The work addresses a critical challenge in power system operations: efficiently determining which generation units to commit and how to optimally dispatch them while maintaining network reliability constraints for both normal and contingency scenarios. Our efforts: 1. Advance the Understanding of SCUC/SCOPF Modeling: By introducing tighter MIP formulations and leveraging cutting-edge optimization tools (Julia/JuMP, PowerModels.jl), this project has pushed forward the state of the art in efficient power systems scheduling. 2. Enhance Technical and Economic Feasibility: The methods developed provide more accurate and potentially faster solutions to large-scale, realistic scheduling and dispatch problems in electric power systems, which can translate into improved reliability and potentially lower costs for grid operations. 3. Benefit to the Public: Greater efficiency in power system operations leads to cost savings for utilities and end-users. Improved reliability and integration of advanced modeling approaches can facilitate the adoption of clean energy resources and better accommodate uncertainties in renewable generation. Because this technology could impact bulk power markets and reliability, these innovations have far-reaching public benefits in terms of cost savings, reliability, and sustainability.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Recovery Simulator and Analysis Formulation: Mathematical Framework for Enhanced Resilience and Resource Allocation
This report introduces recovery simulator and analysis (RSA), a framework aimed at enhancing the resilience of electrical grids post-disruption. The RSA model leverages an optimization problem formulation that focuses on maximizing the load served (or optionally customers served) through a coordinated and cooptimized recovery of non-black start generation, transmission lines, feeders and substations subject to labor budget constraints. By integrating advanced linear programming techniques, the simulator selects efficient reocovery pathways, optimizing both short-term and long-term grid recovery strategies. The mathematical framework guides decision-making through a comprehensive evaluation of potential recovery actions, factoring in the trade-offs between labor constraints and load (or optionally customer) restoration efficacy. This enables grid operators to simulate diverse outage scenarios and delineate optimal recovery pathways, thereby prioritizing critical repair tasks and ensuring resource allocation is both economical and effective. The intended use case of RSA is to allow planners to explore many recovery scenarios quickly and determine assets most critical across a wide range of scenarios, and therefore strong candidates for hardening or additional investment. RSA might also be used in an operational setting, following a single event, for exploring efficient recovery pathways.
24 POWER TRANSMISSION AND DISTRIBUTION↗